A study of the time complexity of an Elitist Genetic Algorithm used for associating optical observations of MEO and GEO Space Debris

Zittersteijn, Michiel; Vananti, Alessandro; Schildknecht, Thomas; Dolado-Perez, J.C.; Martinot, V. (2015). A study of the time complexity of an Elitist Genetic Algorithm used for associating optical observations of MEO and GEO Space Debris (Unpublished). In: Proceedings of workshop on Key Topics in Orbit Propagation Applied to Space Situational Awareness (KEPASSA). Toulouse, France.

[img] Text
MZ_KEPASSA2015.pdf - Accepted Version
Restricted to registered users only
Available under License Publisher holds Copyright.

Download (146kB)

Currently several thousands of objects are being tracked in the MEO and GEO regions through optical means. The problem faced in this framework is that of Multiple Target Tracking (MTT). In this context both the correct associations among the observations, and the orbits of the objects have to be determined.
The complexity of the MTT problem is defined by its dimension S. Where S stands for the number of ’fences’ used in the problem, each fence consists of a set of observations that all originate from dierent targets. For a dimension of
S ˃ the MTT problem becomes NP-hard. As of now no algorithm exists that can solve an NP-hard problem in an optimal manner within a reasonable (polynomial) computation time. However, there are algorithms that can approximate the solution with a realistic computational e ort. To this end an Elitist Genetic Algorithm is implemented to approximately solve the S ˃ MTT problem in an e cient manner. Its complexity is studied and it is found that an approximate solution can be obtained in a polynomial time. With the advent of improved sensors and a heightened interest in the problem of space debris, it is expected that the number of tracked objects will grow by an order of
magnitude in the near future. This research aims to provide a method that can treat the correlation and orbit determination problems simultaneously, and is able to e ciently process large data sets with minimal manual intervention.

Item Type:

Conference or Workshop Item (Paper)

Division/Institute:

08 Faculty of Science > Institute of Astronomy

UniBE Contributor:

Zittersteijn, Michiel, Vananti, Alessandro, Schildknecht, Thomas

Subjects:

500 Science > 520 Astronomy

Language:

English

Submitter:

Alessandro Vananti

Date Deposited:

15 Dec 2015 08:09

Last Modified:

05 Dec 2022 14:50

BORIS DOI:

10.7892/boris.73956

URI:

https://boris.unibe.ch/id/eprint/73956

Actions (login required)

Edit item Edit item
Provide Feedback